Concept drift is a hot research topic in data stream mining [1]}, incremental learning [2]}, nonstationary learning [3]}, and it can be further extended to machine learning. In the machine learning area, concept drift is a phenomenon in which the statistical properties of the predicted variable change over time in an arbitrary way [4]}. This causes the predictions to become less accurate as time passes, and therefore needs to be detected. Figure REF  shows the four types of concept drift, categorized according to the changed pattern of data distribution [5]}.
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